Generally, common embedded image processing systems for lane and vehicle recognition are usually troubled by a problem that the image recognition rate of those embedded image processing systems can not be evaluated and accessed quantitatively in a scientific manner. This is because that although the conventional embedded image processing systems are used and performed as real-time systems, they can not process images as fast as the speed of images being inputted therein for processing since the calculation algorithm programmed therein for processing those inputted images are usually very complicated, not to mention that the more complex the imputed image is, the slower the processing speed of the embedded image processing systems will be. Consequently, it is hard to recognize an object by performing a pixel-to-pixel comparison between images with respect to the corresponding ground truth of the object in real time. Please refer to FIG. 1, which is a schematic diagram showing how a series of image frames is being processed by a conventional embedded image processing system. In the example shown in FIG. 1, the video containing a series of images to be recognized is recorded at a speed of 30 frames per second by that the elapsed time between two frames is approximately 33 ms, that is, there will be a frame being delivered to the embedded image processing system for processing every 33 ms. However, a certain amount of time is required for the embedded image processing system to recognize each frame. Taking the first frame 10 for instance, the required process time for the embedded image processing system is about 67 ms to 99 ms. Therefore, when the embedded image processing system is ready for processing the next frame, the fourth frame 13 in the video delivered after 99 ms is the one to be processed and the second and the third frames 11, 12 that are respectively being delivered at the time of 34 ms and 67 ms will be lost, i.e. the fourth frame 13 is sampled while the second and the third frames are not. In addition, since the processing times for different frames of different complexities will be different, not only the amount of frames being sampled by the embedded image processing system can not be ascertained, but also it can not ensure whether or not a specific frame in the video is sampled. Thus, for the conventional embedded image processing system, the recognition rate can be very hard to calculate and thus it is difficult to optimize the parameters used in recognizing logic of the embedded image processing system according to the recognition rate.
Moreover, as the video to be processed is usually subjected to multiple analog-digital conversions, the signal quality may deteriorate accordingly. Not to mention that there might be differences between a frame before conversion and the corresponding frame after conversion that is resulted from the multiple conversions. Consequently, it is difficult to achieve a global optimization for the embedded image processing system. Fundamentally, a means capable of calculating the recognition rate for embedded image processing system in an automatic manner is required. In view of this, if the embedded image processing system can be designed to be integrated with an image controlling apparatus for automatically calculating the recognition rate according to recognizing result obtained from the embedded image processing system whose images are provided by the image controlling apparatus, not only each and every frame in the video will be sampled for processing without any one to be lost, but also it is possible to process the frames repetitively according to a statistic model for enabling evaluating quantitatively the image recognition rates in a scientific manners with respect to different calculation algorithms resulting from the statistical model and thereafter to be used as basis for optimizing their corresponding calculation algorithms.
There are already many studies relating to the application of embedded image processing systems, such as a mobile range finder disclosed in U.S. Pat. No. 4,942,533 and a lane recognition system disclosed in U.S. Pat. No. 7,295,682. Nevertheless, the processing algorithms used in aforesaid embedded image processing systems are established basing upon human judgment and assessment that such embedded image processing systems are incapable of assessing and quantifying its performance in an objective and scientific manner. Thus, the actual image recognition rate of such embedded image processing system may greatly depart from the theoretical image recognition rate that is resulting from a processing algorithm optimized by developing engineers using a very small amount of sampled images. Thus, it is in need of a means capable of automatically processing a greatly amount of tested images for calculating image recognition rates accordingly.